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Learning and recalling sequences of actions at the neuronal level and beyond

Abstract

Living organisms rely on repeated sequences of actions to execute many of the important functions of their day-to-day lives. Some are stereotyped over nearly the entire lifespan of organisms, others are learned and recalled on a much more rapid timescale. Numerous studies of the ways in which basic neural network models can learn and recall simple sequences of stimuli or patterns of activity exist, as do behavioral and applied studies examining the psychophysical process of learning sequences of actions. However, the gap between these two levels has not yet been bridged. We aim to bring together experts on neural network modeling, neural plasticity phenomenology, and analysis and implementation of this sort of learning in applied scenarios to begin a discussion that will help to close this gap in levels of abstraction.

Specifically, we aim to first examine the mechanisms of neural plasticity (and associated homeostatic processes) and network structures that allow sequences of stimuli or actions to be learned. We will then examine the network dynamics that allow the recall and executions of such patterns of action. We will then consider larger-scale designs that can integrate this in more than a simplest-test-case scenario. Subsequently, we will engage with sicentists engaged in neuro-inspired robotics who are beginning to integrate such systems into their platforms, potentially in a closed-loop fashion, in order to form a cohesive chain of how these concepts arise from the most fundamental to the fully applied levels.

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